Google Reveals New Insights Into Its AI Mode Search: The Power of Query Fan-Out

In a recent interview, Robby Stein, Google’s VP of Product for Search, offered a closer look into how the Query Fan-Out technique powers AI-driven search experiences in AI Mode, revealing just how expansive and dynamic search has become in the age of large language models (LLMs).

Although the concept of Query Fan-Out has been touched on in past Google blog posts, Stein's commentary provides important technical and strategic context for SEOs, marketers, and digital product teams trying to understand what happens behind the curtain of modern AI-enhanced search.

What Is Query Fan-Out?

Query Fan-Out is a core technique behind Google’s AI Mode, which uses a large language model to interpret a user's query and automatically generate a series of related sub-queries—even ones the user never directly typed.

In Stein’s words:

“If you’re asking a question like ‘things to do in Nashville with a group,’ it may think of a bunch of questions like great restaurants, great bars, things to do if you have kids, and it’ll start Googling basically.”

In effect, AI Mode becomes an intelligent search assistant that fans out multiple background queries behind the scenes, combining results into a single, cohesive answer enriched with links, recommendations, and sometimes interactive content.

This system underpins not only AI Mode, but also Deep Search and parts of the AI Overviews that are slowly becoming standard in Google’s evolving search interface.

The Scale: AI Search for 1.5 Billion Users Monthly

According to Stein, Google's AI-powered search tools are now serving 1.5 billion users per month, incorporating everything from traditional web search to multimodal input (text, images, charts, etc.).

AI Mode leverages vast internal data systems like:

• Google’s Shopping Graph, updated 2 billion times per hour.
• Google Finance for real-time stock and financial data.
• Flight and movie information.
• And over 50 billion products indexed in near real-time.

Stein called Google Search “the largest AI product in the world,” a statement that underscores how deeply LLMs and AI orchestration are now baked into the core of how Google delivers results.

How Deep Search Pushes Boundaries

One particularly compelling insight was Stein’s description of Deep Search, which activates when Google determines a query requires more nuanced reasoning.

Unlike standard search, Deep Search can:

• Run dozens or even hundreds of sub-queries.
• Take several minutes to complete.
• Deliver a composite response that mimics expert-level analysis.

For example, when researching home safes, Stein described how Deep Search retrieved information on fire resistance, insurance implications, product reviews, and safety ratings—all without requiring him to ask for those details.

“It spent, I don’t know, like a few minutes… and gave me this incredible response,” he said.

This kind of behavior edges Google’s search closer to an AI-powered research assistant, not just a keyword-matching engine.

Internal Tools Now Power External AI Results

AI Mode has access to Google’s proprietary data systems, allowing it to make real-time calls to services like Google Finance, flight trackers, and more.

This gives AI Mode a unique edge over competing LLMs, which are often limited to static data or slower APIs. It also means that Google’s AI responses may reflect more up-to-date, actionable insights than traditional search ever could.

For users, this could mean faster and more relevant answers. For businesses, it means new opportunities—and new challenges—for visibility and inclusion.

A Glimpse Into Google's Patent on Thematic Search

Interestingly, Stein's description of query fan-out closely aligns with a Google patent filed in December related to “thematic search.”

The patent outlines:

• Generating sub-queries based on inferred themes.
• Grouping results by topic.
• Summarizing across multiple sources using a language model.

The implication? Google is no longer just retrieving the “best match.” It's organizing information around intent and topic clusters, creating a more holistic response engine powered by AI inference.

Editorial Insight: This may explain why some publishers are seeing less traffic even when their content is still indexed—Google may be summarizing their content into AI responses without necessarily linking directly to it.

The Blurred Line Between Queries and Results

As Google’s AI mode evolves, the boundaries between queries, sub-queries, and final answers are becoming more complex.

This shift poses a paradigm challenge for SEOs and digital marketers: what does it mean to “rank” in a world where Google synthesizes answers from multiple sources, possibly without attribution?

New Search, New Strategy

In a traditional model, ranking for a single keyword or phrase could drive thousands of clicks. But in a fan-out model, the AI is pulling signals from across the web, prioritizing context, trustworthiness, and structure over keyword density or link authority alone.

Marketers may need to pivot:

• From keyword optimization → to topic authority.
• From landing pages → to information hubs.
• From click-centric metrics → to inclusion in AI-generated contexts.

The Future of Search Is Fragmented—and Powerful

Robby Stein’s deep dive into Query Fan-Out reveals a significant transformation in how Google understands and delivers content. Search is no longer a one-to-one experience. It’s many-to-one, many-to-many—a cloud of AI-curated insights stitched together from across the web.

For content creators, brands, and SEOs, the opportunity lies in understanding this system not as a threat, but as a new ecosystem. If you want to be included in the AI-generated answer space, it’s time to optimize for context, completeness, and clarity.